Ahlgren, Fredrik

Abstract [en]

In the context of reducing both greenhouse gases and hazardous emissions, the shipping sector faces a major challenge as it is currently responsible for 11% of the transport sector’s anthropogenic greenhouse gas emissions. Even as emissions reductions are needed, the demand for the transport sector rises exponentially every year. This thesis aims to investigate the potential to use ships’ existing internal energy systems more efficiently. The thesis focusses on making existing ships in real operating conditions more efficient based logged machinery data. This dissertation presents results that can make ship more energy efficient by utilising waste heat recovery and machine learning tools. A significant part of this thesis is based on data from a cruise ship in the Baltic Sea, and an extensive analysis of the ship’s internal energy system was made from over a year’s worth of data. The analysis included an exergy analysis, which also considers the usability of each energy flow. In three studies, the feasibility of using the waste heat from the engines was investigated, and the results indicate that significant measures can be undertaken with organic Rankine cycle devices. The organic Rankine cycle was simulated with data from the ship operations and optimised for off-design conditions, both regarding system design and organic fluid selection. The analysis demonstrates that there are considerable differences between the real operation of a ship and what it was initially designed for. In addition, a large two-stroke marine diesel was integrated into a simulation with an organic Rankine cycle, resulting in an energy efficiency improvement of 5%. This thesis also presents new methods of employing machine learning to predict energy consumption. Machine learning algorithms are readily available and free to use, and by using only a small subset of data points from the engines and existing fuel flow meters, the fuel consumption could be predicted with good accuracy. These results demonstrate a potential to improve operational efficiency without installing additional fuel meters. The thesis presents results concerning how data from ships can be used to further analyse and improve their efficiency, by using both add-on technologies for waste heat recovery and machine learning applications.

Thern, Marcus

Abstract [en]

Maritime transportation is a significant contributor to SOx,NOx, and particle matter (PM) emissions, and to a lesser extent, of CO2. Recently, new regulations are being enforced in special geographical areas to limit the amount of emissions from the ships. This fact, together with the high fuel prices, is driving the marine industry toward the improvement of the energy efficiency of ships. Although more sophisticated and complex engine designs can improve significantly of the energy systems on ships, waste heat recovery arises as the most effective technique for the reduction of the energy consump- tion. In this sense, it is estimated that around 50% of the total energy from the fuel con- sumed in a ship is wasted and rejected through liquid and gas streams. The primary heat sources for waste heat recovery are the engine exhaust and coolant. In this work, we present a study on the integration of an organic Rankine cycle (ORC) in an existing ship, for the recovery of the main and auxiliary engines (AE) exhaust heat. Experimental data from the engines on the cruise ship M/S Birka Stockholm were logged during a port-to- port cruise from Stockholm to Mariehamn, over a period of 4 weeks. The ship has four main engines (ME) W€artsil€ a 5850kW for propulsion, and four AE 2760kW which areused for electrical generation. Six engine load conditions were identified depending on the ship’s speed. The speed range from 12 to 14 kn was considered as the design condi- tion for the ORC, as it was present during more than 34% of the time. In this study, the average values of the engines exhaust temperatures and mass flow rates, for each load case, were used as inputs for a model of an ORC. The main parameters of the ORC, including working fluid and turbine configuration, were optimized based on the criteria of maximum net power output and compactness of the installation components. Results from the study showed that an ORC with internal regeneration using benzene as working fluid would yield the greatest average net power output over the operating time. For this situation, the power production of the ORC would represent about 22% of the total elec- tricity consumption on board. These data confirmed the ORC as a feasible and promisingtechnology for the reduction of fuel consumption and CO2 emissions of existing ships.

Abstract [en]

In recent years, machine learning has evolved in a fast pace as both algorithms and computing power are constantly improving. In this study, a machine learning model for predicting the fuel oil consumption from engine data has been developed for a cruise ship operating in the Baltic Sea. The cruise ship is equipped with legacy volume flow meters and newly installed mass flow meters, as well as an extensive set of logged time series data from the machinery logging system. The model is developed using state-of-the-art Auto Machine Learning tools, which optimises both the model hyper parameters and the model selection by using genetic algorithms. To further increase the model accuracy, a pipeline of different models and pre-processing algorithms is evaluated. An extensive model trained for a certain system can be used for optimisation simulation, as well as online energy efficiency prediction. As the models automatically adapt to noisy sensor data and thus function as a watermark of the machinery system, these algorithms show a potential in predicting ship energy efficiency without installation of additional mass flow meters. All tools used in this study are Open Source tools written in Python and can be applied on board. The study shows great potential for utilising large amounts of already available sensor data for improving the accuracy of the predicted ship energy consumption.

Abstract [en]

This study demonstrates a method for predicting the dynamic fuel consumption on board ships using automated machine learning algorithms, fed only with data for larger time intervals from 12 hours up to 96 hours. The machine learning algorithm trained on dynamic data from shorter time intervals of the engine features together with longer time interval data for the fuel consumption. To give the operator and ship owner real-time energy efficiency statistics, it is essential to be able to predict the dynamic fuel oil consumption. The conventional approach to getting these data is by installing additional mass flow meters, but these come with added cost and complexity. In this study, we propose a machine learning approach using auto machine learning optimisation, with already available data from the machinery logging system.

Abstract [en]

The vast majority of ships trafficking the oceans are fuelled by residual oil with high content of sulphur, which produces sulphur oxides (SOx) when combusted. Additionally, the high pressures and temperatures in modern diesel engines also produce nitrogen oxides (NOx). These emissions are both a hazard to health and the local environment, and regulations enforced by the International Maritime Organization (IMO) are driving the maritime sector towards the use of either distillate fuels containing less sulphur, or the use of exhaust gas cleaning devices.TwocommontechniquesforremovingSOx andlimitingNOx aretheopen loop wet scrubber and exhaust gas recirculation (EGR). A scrubber and EGR installation reduces the overall efficiency of the system as it needs significant pumping power, which means that the exhaust gases are cleaner but at the expense of higher CO2 emissions. In this paper we propose a method to integrate an exhaust gas cleaning device for both NOx and SOx with an organic Rankine cycle for waste heat recovery, thereby enhancing the system efficiency. We investigate three ORC configurations, integrated with the energy flows from both an existing state-of-the-art EGR system and an additional open loop wet scrubber.

Genrup, Magnus

Abstract [en]

In this work we present the quasi-steady state simulation of a regenerative organic Rankine cycle (ORC)integrated in a passenger vessel, over a standard round trip. The study case is the M/S Birka Stockholmcruise ship, which covers a daily route between Stockholm (Sweden) and Mariehamn (Finland).Experimental data of the exhaust gas temperatures, engine loads, and electricity demand on board werelogged over a period of four weeks. These data where used as inputs for a simulation model of an ORC forwaste heat recovery of the exhaust gases. A quasi-steady state simulation was carried out on an offdesignmodel, based on optimized design conditions, to estimate the average net power production ofthe ship over a round trip. The maximum net power production of the ORC during the round trip wasestimated to supply approximately 22% of the total power demand on board. The results showed apotential for ORC as a solution for the maritime transport sector to accomplish the new and morerestrictive regulations on emissions, and to reduce the total fuel consumption.

Andersson, Karin

Abstract [en]

In recent years, the International Maritime Organization agreed on aiming to reduce shipping’s greenhouse gas emissions by 50% with respect to 2009 levels. Meanwhile, cruise ship tourism is growing at a fast pace, making the challenge of achieving this goal even harder. The complexity of the energy system of these ships makes them of particular interest from an energy systems perspective. To illustrate this, we analyzed the energy and exergy flow rates of a cruise ship sailing in the Baltic Sea based on measurements from one year of the ship’s operations. The energy analysis allows identifying propulsion as the main energy user (46% of the total) followed by heat (27%) and electric power (27%) generation; the exergy analysis allowed instead identifying the main inefficiencies of the system: while exergy is primarily destroyed in all processes involving combustion (76% of the total), the other main causes of exergy destruction are the turbochargers, the heat recovery steam generators, the steam heaters, the preheater in the accommodation heating systems, the sea water coolers, and the electric generators; the main exergy losses take place in the exhaust gas of the engines not equipped with heat recovery devices. The application of clustering of the ship’s operations based on the concept of typical operational days suggests that the use of five typical days provides a good approximation of the yearly ship’s operations and can hence be used for the design and optimization of the energy systems of the ship.